Sequence alignment Bayesian

The Bayesian alternative to fixed parameters is to define a probability distribution for the parameters and simulate the joint posterior distribution of the sequence alignment and the parameters with a suitable prior distribution. How can varying the similarity matrix... 

A Zellner. An Introduction to Bayesian Inference in Econometrics. New York Wiley, 197L I Zhu, IS Em, CE Lawrence. Bayesian adaptive sequence alignment algorithms. Bioinformat-ics 14 25 -39, 1998. 

Liu, J., Neuwald, A., and Lawrence, C. (1995) Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Amer. Stat. Assoc. 90, 1156-1170. 

Zhu et al. [15] and Liu and Lawrence [61] formalized this argument with a Bayesian analysis. They are seeking a joint posterior probability for an alignment A, a choice of distance matrix 0, and a vector of gap parameters. A, given the data, i.e., the sequences to be aligned p(A, 0, A / i, R2). The Bayesian likelihood and prior for this posterior distribution is... 

The likelihood function is an expression for p(a t, n, C), which is the probability of the sequence a (of length n) given a particular alignment t to a fold C. The expression for the likelihood is where most tlireading algorithms differ from one another. Since this probability can be expressed in terms of a pseudo free energy, p(a t, n, C) x exp[—/(a, t, C)], any energy function that satisfies this equation can be used in the Bayesian analysis described above. The normalization constant required is akin to a partition function, such that... 

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